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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.transforms.cv.video</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from __future__ import annotations

import random
from typing import Any, Dict, Iterable, List, Union

import torch
from silk.transforms.abstract import Transform
from pytorchvideo.data.video import Video


class VideoToImageBatch(Transform):
    &#34;&#34;&#34;Transform PyTorchVideo video clip data as a batch of images.&#34;&#34;&#34;

    def __call__(self, video_data: Dict[str, Any]) -&gt; torch.Tensor:
        &#34;&#34;&#34;

        Parameters
        ----------
        video_data : Dict[str, Any]
            Dictionay which contains a key &#34;video&#34; mapping to a CxTxHxW tensor.

        Returns
        -------
        torch.Tensor
            Batch of images as a TxCxHxW tensor.
        &#34;&#34;&#34;
        video = video_data[&#34;video&#34;]
        video = video.permute(1, 0, 2, 3)  # CTHW -&gt; TCHW
        return video


class Stream(Iterable):
    &#34;&#34;&#34;Create an iterable streaming clips of video data.&#34;&#34;&#34;

    def __init__(
        self,
        video: Video,
        clip_duration: float,
        clip_transform: Transform = None,
        **get_clip_kwargs: Dict[str, Any],
    ) -&gt; None:
        &#34;&#34;&#34;
        Parameters
        ----------
        video : Video
            PyTorchVideo video instance to stream.
        clip_duration : float
            Maximum duration (in seconds) of the returned clip at every iteration.
        clip_transform : Transform, optional
            Optional transform to apply to each clip, by default None
        get_clip_kwargs : Dict[str, Any]
            Arguments to pass to the underlying video `get_clip` method.
        &#34;&#34;&#34;
        super().__init__()
        self._clip_duration = clip_duration
        self._clip_transform = clip_transform
        self._video = video
        self._get_clip_kwargs = get_clip_kwargs

    def __iter__(self):
        current_time = 0.0
        while current_time &lt; self._video.duration:
            next_time = min(
                self._video.duration,
                current_time + self._clip_duration,
            )
            video_data = self._video.get_clip(
                current_time,
                next_time,
                **self._get_clip_kwargs,
            )
            current_time = next_time

            if self._clip_transform:
                video_data = self._clip_transform(video_data)

            yield video_data

    @property
    def video(self):
        return self._video


class Streamed(Transform):
    &#34;&#34;&#34;Apply a video transform in a streamed fashion (useful for large videos).&#34;&#34;&#34;

    def __init__(
        self,
        clip_duration: float,
        clip_transform: Transform = None,
        return_iterable: bool = False,
        **get_clip_kwargs,
    ) -&gt; None:
        &#34;&#34;&#34;
        Parameters
        ----------
        clip_duration : float
            Maximum duration (in seconds) of the transformed clip at every iteration.
        clip_transform : Transform, optional
            Optional transform to apply to each clip, by default None.
        return_iterable : bool, optional
            Decides if transform should return an iterable (more control over looping) or the iterated result, by default False.
        &#34;&#34;&#34;
        super().__init__()
        self._clip_transform = clip_transform
        self._clip_duration = clip_duration
        self._return_iterable = return_iterable
        self._get_clip_kwargs = get_clip_kwargs

    def __call__(self, video: Video) -&gt; Union[Stream, List[Any]]:
        stream = Stream(
            video,
            self._clip_duration,
            self._clip_transform,
            **self._get_clip_kwargs,
        )
        if self._return_iterable:
            return stream
        return list(stream)


class GetClipVideoWrapper(Transform):
    &#34;&#34;&#34;Transform that extracts a clip from a video.&#34;&#34;&#34;

    class WrappedVideo(Video):
        def __init__(self, video: Video, **get_clip_kwargs) -&gt; None:
            self._video = video
            self._get_clip_kwargs = get_clip_kwargs

        @property
        def video(self):
            return self._video

        @property
        def duration(self):
            return self._video.duration

        def get_clip(
            self,
            start_sec,
            end_sec,
            **get_clip_kwargs,
        ):
            kwargs = {**self._get_clip_kwargs, **get_clip_kwargs}
            return self._video.get_clip(start_sec, end_sec, **kwargs)

    def __init__(self, **get_clip_kwargs) -&gt; None:
        super().__init__()
        self._get_clip_kwargs = get_clip_kwargs

    def __call__(self, video: Video) -&gt; Video:
        return GetClipVideoWrapper.WrappedVideo(video, **self._get_clip_kwargs)


class GetAllClip(Transform):
    &#34;&#34;&#34;Get entire video tensor.&#34;&#34;&#34;

    def __call__(self, video: Video):
        return video.get_clip(0, video.duration)


class UniformTemporalSubsampleFrameFilter:
    &#34;&#34;&#34;Subsample frames uniformly.&#34;&#34;&#34;

    def __init__(self, num_samples) -&gt; None:
        self._num_samples: int = num_samples

    def __call__(self, idx: List[int]) -&gt; List[int]:
        t = len(idx)
        indices = torch.linspace(0, t - 1, self._num_samples)
        indices = indices.long()
        return [idx[i] for i in indices]


class RandomTemporalShuffleFrameFilter:
    &#34;&#34;&#34;Random subsampling of frames.&#34;&#34;&#34;

    def __init__(self, num_samples) -&gt; None:
        self._num_samples: int = num_samples

    def __call__(self, idx: List[int]) -&gt; List[int]:
        random.shuffle(idx)
        return idx[: self._num_samples]</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.transforms.cv.video.GetAllClip"><code class="flex name class">
<span>class <span class="ident">GetAllClip</span></span>
</code></dt>
<dd>
<div class="desc"><p>Get entire video tensor.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GetAllClip(Transform):
    &#34;&#34;&#34;Get entire video tensor.&#34;&#34;&#34;

    def __call__(self, video: Video):
        return video.get_clip(0, video.duration)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.transforms.cv.video.GetAllClip.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.transforms.cv.video.GetAllClip.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></b></code>:
<ul class="hlist">
<li><code><a title="silk.transforms.abstract.Transform.forward" href="../abstract.html#silk.transforms.abstract.Transform.forward">forward</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="silk.transforms.cv.video.GetClipVideoWrapper"><code class="flex name class">
<span>class <span class="ident">GetClipVideoWrapper</span></span>
<span>(</span><span>**get_clip_kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Transform that extracts a clip from a video.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GetClipVideoWrapper(Transform):
    &#34;&#34;&#34;Transform that extracts a clip from a video.&#34;&#34;&#34;

    class WrappedVideo(Video):
        def __init__(self, video: Video, **get_clip_kwargs) -&gt; None:
            self._video = video
            self._get_clip_kwargs = get_clip_kwargs

        @property
        def video(self):
            return self._video

        @property
        def duration(self):
            return self._video.duration

        def get_clip(
            self,
            start_sec,
            end_sec,
            **get_clip_kwargs,
        ):
            kwargs = {**self._get_clip_kwargs, **get_clip_kwargs}
            return self._video.get_clip(start_sec, end_sec, **kwargs)

    def __init__(self, **get_clip_kwargs) -&gt; None:
        super().__init__()
        self._get_clip_kwargs = get_clip_kwargs

    def __call__(self, video: Video) -&gt; Video:
        return GetClipVideoWrapper.WrappedVideo(video, **self._get_clip_kwargs)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.transforms.cv.video.GetClipVideoWrapper.WrappedVideo"><code class="name">var <span class="ident">WrappedVideo</span></code></dt>
<dd>
<div class="desc"><p>Video provides an interface to access clips from a video container.</p></div>
</dd>
<dt id="silk.transforms.cv.video.GetClipVideoWrapper.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.transforms.cv.video.GetClipVideoWrapper.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></b></code>:
<ul class="hlist">
<li><code><a title="silk.transforms.abstract.Transform.forward" href="../abstract.html#silk.transforms.abstract.Transform.forward">forward</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="silk.transforms.cv.video.RandomTemporalShuffleFrameFilter"><code class="flex name class">
<span>class <span class="ident">RandomTemporalShuffleFrameFilter</span></span>
<span>(</span><span>num_samples)</span>
</code></dt>
<dd>
<div class="desc"><p>Random subsampling of frames.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class RandomTemporalShuffleFrameFilter:
    &#34;&#34;&#34;Random subsampling of frames.&#34;&#34;&#34;

    def __init__(self, num_samples) -&gt; None:
        self._num_samples: int = num_samples

    def __call__(self, idx: List[int]) -&gt; List[int]:
        random.shuffle(idx)
        return idx[: self._num_samples]</code></pre>
</details>
</dd>
<dt id="silk.transforms.cv.video.Stream"><code class="flex name class">
<span>class <span class="ident">Stream</span></span>
<span>(</span><span>video: Video, clip_duration: float, clip_transform: Transform = None, **get_clip_kwargs: Dict[str, Any])</span>
</code></dt>
<dd>
<div class="desc"><p>Create an iterable streaming clips of video data.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>video</code></strong> :&ensp;<code>Video</code></dt>
<dd>PyTorchVideo video instance to stream.</dd>
<dt><strong><code>clip_duration</code></strong> :&ensp;<code>float</code></dt>
<dd>Maximum duration (in seconds) of the returned clip at every iteration.</dd>
<dt><strong><code>clip_transform</code></strong> :&ensp;<code>Transform</code>, optional</dt>
<dd>Optional transform to apply to each clip, by default None</dd>
<dt><strong><code>get_clip_kwargs</code></strong> :&ensp;<code>Dict[str, Any]</code></dt>
<dd>Arguments to pass to the underlying video <code>get_clip</code> method.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Stream(Iterable):
    &#34;&#34;&#34;Create an iterable streaming clips of video data.&#34;&#34;&#34;

    def __init__(
        self,
        video: Video,
        clip_duration: float,
        clip_transform: Transform = None,
        **get_clip_kwargs: Dict[str, Any],
    ) -&gt; None:
        &#34;&#34;&#34;
        Parameters
        ----------
        video : Video
            PyTorchVideo video instance to stream.
        clip_duration : float
            Maximum duration (in seconds) of the returned clip at every iteration.
        clip_transform : Transform, optional
            Optional transform to apply to each clip, by default None
        get_clip_kwargs : Dict[str, Any]
            Arguments to pass to the underlying video `get_clip` method.
        &#34;&#34;&#34;
        super().__init__()
        self._clip_duration = clip_duration
        self._clip_transform = clip_transform
        self._video = video
        self._get_clip_kwargs = get_clip_kwargs

    def __iter__(self):
        current_time = 0.0
        while current_time &lt; self._video.duration:
            next_time = min(
                self._video.duration,
                current_time + self._clip_duration,
            )
            video_data = self._video.get_clip(
                current_time,
                next_time,
                **self._get_clip_kwargs,
            )
            current_time = next_time

            if self._clip_transform:
                video_data = self._clip_transform(video_data)

            yield video_data

    @property
    def video(self):
        return self._video</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>collections.abc.Iterable</li>
<li>typing.Generic</li>
</ul>
<h3>Instance variables</h3>
<dl>
<dt id="silk.transforms.cv.video.Stream.video"><code class="name">var <span class="ident">video</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def video(self):
    return self._video</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.transforms.cv.video.Streamed"><code class="flex name class">
<span>class <span class="ident">Streamed</span></span>
<span>(</span><span>clip_duration: float, clip_transform: Transform = None, return_iterable: bool = False, **get_clip_kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Apply a video transform in a streamed fashion (useful for large videos).</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>clip_duration</code></strong> :&ensp;<code>float</code></dt>
<dd>Maximum duration (in seconds) of the transformed clip at every iteration.</dd>
<dt><strong><code>clip_transform</code></strong> :&ensp;<code>Transform</code>, optional</dt>
<dd>Optional transform to apply to each clip, by default None.</dd>
<dt><strong><code>return_iterable</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>Decides if transform should return an iterable (more control over looping) or the iterated result, by default False.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Streamed(Transform):
    &#34;&#34;&#34;Apply a video transform in a streamed fashion (useful for large videos).&#34;&#34;&#34;

    def __init__(
        self,
        clip_duration: float,
        clip_transform: Transform = None,
        return_iterable: bool = False,
        **get_clip_kwargs,
    ) -&gt; None:
        &#34;&#34;&#34;
        Parameters
        ----------
        clip_duration : float
            Maximum duration (in seconds) of the transformed clip at every iteration.
        clip_transform : Transform, optional
            Optional transform to apply to each clip, by default None.
        return_iterable : bool, optional
            Decides if transform should return an iterable (more control over looping) or the iterated result, by default False.
        &#34;&#34;&#34;
        super().__init__()
        self._clip_transform = clip_transform
        self._clip_duration = clip_duration
        self._return_iterable = return_iterable
        self._get_clip_kwargs = get_clip_kwargs

    def __call__(self, video: Video) -&gt; Union[Stream, List[Any]]:
        stream = Stream(
            video,
            self._clip_duration,
            self._clip_transform,
            **self._get_clip_kwargs,
        )
        if self._return_iterable:
            return stream
        return list(stream)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.transforms.cv.video.Streamed.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.transforms.cv.video.Streamed.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></b></code>:
<ul class="hlist">
<li><code><a title="silk.transforms.abstract.Transform.forward" href="../abstract.html#silk.transforms.abstract.Transform.forward">forward</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="silk.transforms.cv.video.UniformTemporalSubsampleFrameFilter"><code class="flex name class">
<span>class <span class="ident">UniformTemporalSubsampleFrameFilter</span></span>
<span>(</span><span>num_samples)</span>
</code></dt>
<dd>
<div class="desc"><p>Subsample frames uniformly.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class UniformTemporalSubsampleFrameFilter:
    &#34;&#34;&#34;Subsample frames uniformly.&#34;&#34;&#34;

    def __init__(self, num_samples) -&gt; None:
        self._num_samples: int = num_samples

    def __call__(self, idx: List[int]) -&gt; List[int]:
        t = len(idx)
        indices = torch.linspace(0, t - 1, self._num_samples)
        indices = indices.long()
        return [idx[i] for i in indices]</code></pre>
</details>
</dd>
<dt id="silk.transforms.cv.video.VideoToImageBatch"><code class="flex name class">
<span>class <span class="ident">VideoToImageBatch</span></span>
</code></dt>
<dd>
<div class="desc"><p>Transform PyTorchVideo video clip data as a batch of images.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class VideoToImageBatch(Transform):
    &#34;&#34;&#34;Transform PyTorchVideo video clip data as a batch of images.&#34;&#34;&#34;

    def __call__(self, video_data: Dict[str, Any]) -&gt; torch.Tensor:
        &#34;&#34;&#34;

        Parameters
        ----------
        video_data : Dict[str, Any]
            Dictionay which contains a key &#34;video&#34; mapping to a CxTxHxW tensor.

        Returns
        -------
        torch.Tensor
            Batch of images as a TxCxHxW tensor.
        &#34;&#34;&#34;
        video = video_data[&#34;video&#34;]
        video = video.permute(1, 0, 2, 3)  # CTHW -&gt; TCHW
        return video</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.transforms.cv.video.VideoToImageBatch.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.transforms.cv.video.VideoToImageBatch.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="silk.transforms.abstract.Transform" href="../abstract.html#silk.transforms.abstract.Transform">Transform</a></b></code>:
<ul class="hlist">
<li><code><a title="silk.transforms.abstract.Transform.forward" href="../abstract.html#silk.transforms.abstract.Transform.forward">forward</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.transforms.cv" href="index.html">silk.transforms.cv</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.transforms.cv.video.GetAllClip" href="#silk.transforms.cv.video.GetAllClip">GetAllClip</a></code></h4>
<ul class="">
<li><code><a title="silk.transforms.cv.video.GetAllClip.dump_patches" href="#silk.transforms.cv.video.GetAllClip.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.transforms.cv.video.GetAllClip.training" href="#silk.transforms.cv.video.GetAllClip.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.GetClipVideoWrapper" href="#silk.transforms.cv.video.GetClipVideoWrapper">GetClipVideoWrapper</a></code></h4>
<ul class="">
<li><code><a title="silk.transforms.cv.video.GetClipVideoWrapper.WrappedVideo" href="#silk.transforms.cv.video.GetClipVideoWrapper.WrappedVideo">WrappedVideo</a></code></li>
<li><code><a title="silk.transforms.cv.video.GetClipVideoWrapper.dump_patches" href="#silk.transforms.cv.video.GetClipVideoWrapper.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.transforms.cv.video.GetClipVideoWrapper.training" href="#silk.transforms.cv.video.GetClipVideoWrapper.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.RandomTemporalShuffleFrameFilter" href="#silk.transforms.cv.video.RandomTemporalShuffleFrameFilter">RandomTemporalShuffleFrameFilter</a></code></h4>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.Stream" href="#silk.transforms.cv.video.Stream">Stream</a></code></h4>
<ul class="">
<li><code><a title="silk.transforms.cv.video.Stream.video" href="#silk.transforms.cv.video.Stream.video">video</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.Streamed" href="#silk.transforms.cv.video.Streamed">Streamed</a></code></h4>
<ul class="">
<li><code><a title="silk.transforms.cv.video.Streamed.dump_patches" href="#silk.transforms.cv.video.Streamed.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.transforms.cv.video.Streamed.training" href="#silk.transforms.cv.video.Streamed.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.UniformTemporalSubsampleFrameFilter" href="#silk.transforms.cv.video.UniformTemporalSubsampleFrameFilter">UniformTemporalSubsampleFrameFilter</a></code></h4>
</li>
<li>
<h4><code><a title="silk.transforms.cv.video.VideoToImageBatch" href="#silk.transforms.cv.video.VideoToImageBatch">VideoToImageBatch</a></code></h4>
<ul class="">
<li><code><a title="silk.transforms.cv.video.VideoToImageBatch.dump_patches" href="#silk.transforms.cv.video.VideoToImageBatch.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.transforms.cv.video.VideoToImageBatch.training" href="#silk.transforms.cv.video.VideoToImageBatch.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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